Search Results (81)

Fully polarimetric SAR provides richer scattering information than single-polarisation imaging, but multichannel sparse image formation can be computationally and memory demanding, especially when channels are processed jointly. In our previous work, we introduced Orthogonal Matching Pursuit 2D Fully Polarimetric (OMP2D-FP), a greedy reconstruction algorithm that enforces a shared spatial support across polarimetric channels while exploiting a separable 2D formulation to avoid vectorisation and reduce computational burden and memory footprint relative to vectorised OMP-based formulations. In this paper, we extend its validation to real measurements and further develop its theoretical foundations by recasting the atom-selection step as a detection–estimation problem, thereby defining a cumulative objective function (COF) design space that enables the incorporation of disturbance statistics and prior knowledge into sparse recovery. Experiments on fully polarimetric SAR data of a T-72 tank over a wide range of aspect angles, SNR levels, and measurement percentages show that joint support selection improves reconstruction fidelity and polarimetric information preservation over independent per-channel processing, with particularly clear gains under challenging conditions. Preliminary applications of the COF framework (a whitening COF incorporating polarimetric clutter statistics and a mask-based COF incorporating spatial prior knowledge) yield encouraging results, motivating further systematic investigation of adaptive COF designs. Full article

Azimuth multi-channel (AMC) Synthetic Aperture Ladar (SAL) is a promising technique for overcoming the inherent trade-off between azimuth resolution and swath width in single-channel SAL, by replacing temporal sampling with spatial sampling. However, due to the micron-scale wavelength, AMC SAL is extremely sensitive to non-cooperative target motion: even millimeter-level radial velocities can induce significant inter-channel phase deviations, leading to severe azimuth ambiguities (false targets). To address this critical issue, a motion-compensated reconstruction framework for AMC SAL is proposed for micro-motion targets. The relationship between target radial motion and inter-channel phase deviations is theoretically derived, and a parametric strategy based on a Minimum Azimuth Ambiguity-to-Signal Ratio (MAASR) criterion is proposed to estimate the radial velocity. Simulation results demonstrate that the uncompensated processing suffers from strong ambiguities (AASR = −2.90 dB) and a notable azimuth position shift (−42 samples), whereas the proposed method suppresses false targets to the noise floor (<−40 dB) and corrects the position error. These simulation results indicate that the proposed method enables AMC SAL imaging for the non-cooperative moving target with millimeter-level radial velocity. Full article

Few-shot object detection suffers from limited annotations, redundant background interference, insufficient feature interaction, and severe sample imbalance. Existing meta-learning-based methods extract class prototypes from support images but often fail to effectively suppress background noise or align channel-wise features between support and query branches. To address these issues, we propose a few-shot object detection method based on background reconstruction and multi-channel interactive feature fusion. First, a background reconstruction module is designed to suppress redundant background interference by applying random region masking to support set images, thereby generating robust class prototype features that are resistant to background noise. Second, a multi-channel interactive feature fusion module is designed, which leverages depthwise separable convolution to enable effective channel-wise feature interaction and information alignment between support class prototypes and query features, thereby enhancing cross-branch feature interaction and fusion. Finally, to address the uneven sample distribution and the foreground–background imbalance in few-shot scenarios, we proposed a category-aware weighted loss. By appropriately weighting the contributions of different object categories and background samples, the proposed loss encourages balanced optimization, resulting in faster convergence and improved detection performance. Experimental results demonstrate that the proposed method improves detection accuracy and generalization performance under few-shot settings. On the Pascal VOC dataset (Split1), the proposed method achieves 45.1%, 62.9%, and 67.1% under 1-shot, 3-shot, and 10-shot settings, respectively, outperforming the baseline; consistent improvements are also observed on the MS COCO dataset and the DIOR dataset. Full article

Land seismic data are always contaminated by surface waves, which demonstrate strong energy, low velocity, and long vibrations. Such noises often mask deep effective reflections, seriously reducing the data’s signal-to-noise ratio while limiting the imaging accuracy of complex deep structures and the efficiency of hydrocarbon reservoir identification. To address this critical technical bottleneck, this paper proposes a surface wave joint reconstruction method based on stationary phase analysis, combining the cross-correlation seismic interferometry method with the convolutional seismic interferometry method. This approach integrates cross-correlation and convolutional seismic interferometry techniques to achieve coordinated reconstruction of surface waves in both shot and receiver domains while introducing adaptive weight factors to optimize the reconstruction process and reduce interference from erroneous data. As a purely data-driven framework, this method does not rely on underground medium velocity models, achieving efficient noise reduction by adaptively removing reconstructed surface waves through multi-channel matched filtering. Application validation with field seismic data from the piedmont regions of western China demonstrates that this method effectively suppresses high-energy surface waves, significantly restores effective signals, improves the signal-to-noise ratio of seismic data, and greatly enhances the clarity of coherent events in stacked profiles. This study provides a reliable technical approach for noise reduction in seismic data under complex near-surface conditions, particularly suitable for hydrocarbon exploration in regions with developed scattering sources such as mountainous areas in western China. It holds significant practical application value and broad dissemination potential for advancing deep hydrocarbon resource exploration and improving the quality of complex structural imaging. Full article

Weak-signal fluorescence channels (e.g., 4′,6-diamidino-2-phenylindole (DAPI)) often fail to provide reliable structural details due to low signal-to-noise ratio (SNR) and insufficient high-frequency information, limiting the ability of single-channel super-resolution methods to restore edge continuity and texture. This study proposes a multi-channel guided super-resolution method based on SwinIR, utilizing the high-SNR fluorescein isothiocyanate (FITC) channel as a structural reference. Dual-channel adaptation is implemented at the model input layer, enabling the window attention mechanism to fuse cross-channel correlation information and enhance the structural recovery capability of weak-signal channels. To address the loss of high-frequency information in weak-signal imaging, we introduce a frequency-domain consistency loss: this mechanism constrains spectral consistency between the predicted and true images in the Fourier domain, improving the clarity of fine-structure reconstruction. Experimental results on the DAPI channel demonstrate significant improvements: PSNR increases from 27.05 dB to 44.98 dB, and SSIM rises from 0.763 to 0.960. Visual analysis indicates that this method restores more continuous nuclear edges and weak textural details while suppressing background noise; frequency-domain results reduce the minimum resolvable feature size from approximately 1.5 μm to 0.8 μm. In summary, multi-channel structural information provides an effective and physically interpretable deep learning approach for super-resolution reconstruction of weak-signal fluorescence images. Full article

Surface Plasmon Resonance imaging (SPRi) is a powerful label-free technique for high-throughput biochemical analysis. Wavelength modulation is particularly suitable for SPRi due to its wide dynamic range and robustness to fabrication tolerances. However, conventional systems relying on tunable filters (e.g., AOTF, LCTF) suffer from high cost, complexity, and limited temporal resolution. To overcome these drawbacks, we developed a rapid wavelength-modulation SPRi system using a multi-LED source and an adaptive second-order fitting (ASF) algorithm. The system covers the 730–805 nm spectrum with five LEDs. The ASF algorithm first performs a coarse full-spectrum scan to locate the resonance wavelength, then dynamically selects an optimal three-LED subset for fast second-order fitting, enabling accurate reconstruction of resonance wavelength without mechanical scanning. This approach significantly reduces cost and complexity while achieving a scanning cycle of 105 ms, RI resolution of 5.54 × 10⁻⁶ RIU, dynamic range of 0.0241 RIU, and excellent multi-channel consistency. The system has been successfully applied to monitor multi-channel antibody–antigen interactions in real time. Furthermore, it was used to detect cartilage oligomeric matrix protein (COMP) in synovial fluid, where an elevated concentration in an osteoarthritis sample versus a control aligned with its role as a cartilage catabolism marker. This work validates a practical and reliable platform for early diagnosis of osteoarthritis. Full article

HRWS (high resolution and wide swath, HRWS) SAR always suffers channel phase error in the multichannel reconstruction stage and results in a lower imaging quality. The image domain error estimation method can achieve superior performance by utilizing the signal-to-noise ratio (SNR) advantage. Nevertheless, in practice, the inevitable baseline error in HRWS SAR will lead to the inability of multichannel images to be registered in azimuth time and reduction of the channel phase error estimation accuracy. Considering that the joint-pixel model can fully contain the coherent information in such a case, a novel multichannel phase error estimation method is proposed. In this paper, by establishing a multichannel signal model in the image domain, an image domain subspace-based phase error estimation method based on joint-pixel selection and vector construction is derived. The proposed method can weaken the influence of subspace estimation inaccuracy caused by the inaccurate azimuth baseline and avoid the large amount of calculation caused by iterative elimination of baseline error and phase error in traditional algorithms, thus further improving computational efficiency. Simulation experiments and real acquired HRWS SAR data processing validate the estimation accuracy of the proposed method. Full article

Three-dimensional holographic imaging technology is increasingly applied in biomedical detection, materials science, and industrial non-destructive testing. Achieving high-resolution, large-field-of-view, and high-speed three-dimensional imaging has become a significant challenge. This paper proposes and implements a three-dimensional holographic imaging method based on trillion-frame-frequency all-optical multiplexing. This approach combines spatial and temporal multiplexing to achieve multi-channel partitioned acquisition of the light field via a two-dimensional diffraction grating, significantly enhancing the system’s imaging efficiency and dynamic range. The paper systematically derives the theoretical foundation of holographic imaging, establishes a numerical reconstruction model based on angular spectrum propagation, and introduces iterative phase recovery and image post-processing strategies to optimize reproduction quality. Experiments using standard resolution plates and static particle fields validate the proposed method’s imaging performance under static conditions. Results demonstrate high-fidelity reconstruction approaching diffraction limits, with post-processing further enhancing image sharpness and signal-to-noise ratio. This research establishes theoretical and experimental foundations for subsequent dynamic holographic imaging and observation of large-scale complex targets. Full article

The advent of multichannel ground-penetrating radar systems capable of acquiring multiview, multistatic, and multifrequency data is offering new possibilities to improve subsurface imaging performance. However, this raises the need for reconstruction approaches capable of handling such sophisticated configurations and the resulting increase in the data volume. Therefore, the challenge lies in identifying proper measurement configurations that balance image quality with the complexity and duration of data acquisition. As a contribution to this topic, the present paper focuses on a measurement system working in reflection mode and composed of an array of antennas, consisting of a transmitting antenna and several receiving antennas, whose spatial offset is comparable to the probing wavelength. Therefore, for each position of the transmitting antenna, a single-view/multistatic configuration is considered. The imaging task is solved by adopting a linear microwave tomographic approach, which provides a qualitative reconstruction of the investigated scenario. In particular, a 3D inverse scattering problem is tackled for an isotropic, homogeneous, lossless, and non-magnetic medium under the Born approximation, considering both single- and multi-frequency data. A preliminary analysis, referring to a 3D free-space reference scenario, is performed in terms of the spectral content of the scattering operator and the system’s point spread function. Finally, an experimental validation under laboratory conditions is presented in order to verify the expected imaging capability of the inversion approach. Full article

In open-network environments of smart buildings and urban infrastructure, abnormal traffic from security and energy monitoring systems is critical for operational safety and decision reliability. We can develop malware that exploits building automation protocols to simulate attacks involving the falsification or modification of chiller controller commands, thereby endangering the entire network infrastructure. Intrusion detection systems rely on abundant labeled abnormal traffic data to detect attack patterns, improving network system reliability. However, transmitting such data faces two major challenges: single-feature representations fail to capture comprehensive traffic features, limiting the information representation for artificial intelligence (AI)-based detection models, and unconcealed abnormal traffic is easily intercepted by firewalls or intrusion detection systems, hindering cross-departmental sharing. Existing methods struggle to balance feature integrity and transmission stealth, often sacrificing one for the other or relying on easily detectable spatial-domain steganography. To address these gaps, we propose a multi-channel imaging hiding method that reconstructs abnormal traffic into multi-channel images by combining three mappings to generate grayscale images that depict traffic state transitions, dynamic trends, and internal similarity, respectively. These images are combined to enhance feature representation and embedded into frequency-domain adversarial examples, enabling evasion of security devices while preserving traffic integrity. Experimental results demonstrate that our method captures richer information than single-representation approaches, achieving a PSNR of 44.5 dB (a 6.0 dB improvement over existing methods) and an SSIM of 0.97. The high-fidelity reconstructions enabled by these gains facilitate the secure and efficient sharing of abnormal traffic data, thereby enhancing AI-driven security in smart buildings. Full article

In high-resolution and wide-swath (HRWS) synthetic aperture radar (SAR) imaging, the azimuth multi-channel technique effectively suppresses azimuth ambiguity, serving as a reliable approach for achieving wide-swath imaging. However, due to mechanical vibrations of the platform and airflow instabilities, airborne SAR may experience errors in attitude and flight path during operation. Furthermore, errors also exist in the antenna patterns, frequency stability, and phase noise among the azimuth multi-channels. The presence of these errors can cause azimuth multi-channel reconstruction failure, resulting in azimuth ambiguity and significantly degrading the quality of HRWS images. This article presents a novel error correction method for airborne HRWS SAR based on azimuth-variant attitude and range-variant Doppler domain pattern, which simultaneously considers the effects of various errors, including channel attitude errors and Doppler domain antenna pattern errors, on azimuth reconstruction. Attitude errors are the primary cause of azimuth-variant errors between channels. This article uses the vector method and attitude transformation matrix to calculate and compensate for the attitude errors of azimuth multi-channels, and employs the two-dimensional frequency-domain echo interferometry method to calculate the fixed delay errors and fixed phase errors. To better achieve channel error compensation, this scheme also considers the estimation and compensation of Doppler domain antenna pattern errors in wide-swath scenes. Finally, the effectiveness of the proposed scheme is confirmed through simulations and processing of airborne real data. Full article

Micro-motors are essential for power drive systems, and efficient fault diagnosis is crucial to reduce safety risks and economic losses caused by failures. However, the fault signals from micro-motors typically exhibit weak and unclear characteristics. To address this challenge, this paper proposes a novel fault diagnosis method that integrates jump plus AM-FM mode decomposition (JMD), symmetrized dot pattern (SDP) visualization, and an improved convolutional neural network (ICNN). Firstly, we employed the jump plus AM-FM mode decomposition technique to decompose the mixed fault signals, addressing the problem of mode mixing in traditional decomposition methods. Then, the intrinsic mode functions (IMFs) decomposed by JMD serve as the multi-channel inputs for symmetrized dot pattern, constructing a two-dimensional polar coordinate petal image. This process achieves both signal reconstruction and visual enhancement of fault features simultaneously. Finally, this paper designed an ICNN method with LeakyReLU activation function to address the vanishing gradient problem and enhance classification accuracy and training efficiency for fault diagnosis. Experimental results indicate that the proposed JMD-SDP-ICNN method outperforms traditional methods with a significantly superior fault classification accuracy of up to 99.2381%. It can offer a potential solution for the monitoring of electromechanical structures under complex conditions. Full article

High-resolution ISAR (Inverse Synthetic Aperture Radar) imaging plays a crucial role in dynamic target monitoring for aerospace, maritime, and ground surveillance. Among various remote sensing techniques, ISAR is distinguished by its ability to produce high-resolution images of non-cooperative maneuvering targets. To meet the increasing demands for resolution and robustness, modern ISAR systems are evolving toward wideband and multi-channel architectures. In particular, multi-channel configurations based on large-scale receiving arrays have gained significant attention. In such systems, each receiving element functions as an independent spatial channel, acquiring observations from distinct perspectives. These multi-angle measurements enrich the available echo information and enhance the robustness of target imaging. However, this setup also brings significant challenges, including inter-channel coupling, high-dimensional joint signal modeling, and non-Gaussian, mixed-mode interference, which often degrade image quality and hinder reconstruction performance. To address these issues, this paper proposes a Hybrid Variational Bayesian Multi-Interference (HVB-MI) imaging algorithm based on a hierarchical Bayesian framework. The method jointly models temporal correlations and inter-channel structure, introducing a coupled processing strategy to reduce dimensionality and computational complexity. To handle complex noise environments, a Gaussian mixture model (GMM) is used to represent nonstationary mixed noise. A variational Bayesian inference (VBI) approach is developed for efficient parameter estimation and robust image recovery. Experimental results on both simulated and real-measured data demonstrate that the proposed method achieves significantly improved image resolution and noise robustness compared with existing approaches, particularly under conditions of sparse sampling or strong interference. Quantitative evaluation further shows that under the continuous sparse mode with a 75% sampling rate, the proposed method achieves a significantly higher Laplacian Variance (LV), outperforming PCSBL and CPESBL by 61.7% and 28.9%, respectively and thereby demonstrating its superior ability to preserve fine image details. Full article

The purpose of this study was to propose a deep learning-based model for the super-resolution reconstruction of stained light microscopy images. To achieve this, perceptual loss was applied to the generator to reflect multichannel signal intensity, distribution, and structural similarity. A nested U-Net architecture was employed to address the representational limitations of the conventional U-Net. For quantitative evaluation, the peak signal-to-noise ratio (PSNR), structural similarity index (SSIM), and correlation coefficient (CC) were calculated. In addition, intensity profile analysis was performed to assess the model’s ability to restore the boundary signals more precisely. The experimental results demonstrated that the proposed model outperformed both the signal and structural restoration compared to single U-Net and U-Net-based generative adversarial network (GAN) models. Consequently, the PSNR, SSIM, and CC values demonstrated relative improvements of approximately 1.017, 1.023, and 1.010 times, respectively, compared to the input images. In particular, the intensity profile analysis confirmed the effectiveness of the nested U-Net-based generator in restoring cellular boundaries and structures in the stained microscopy images. In conclusion, the proposed model effectively enhanced the resolution of stained light microscopy images acquired in a multichannel format. Full article

Typically, multi-single-element photoacoustic computed tomography (PACT) systems utilize numerous ultrasound transducers arranged in cylindrical or hemispherical configurations for detection, combined with a single diffuse light source or multiple sparse light sources to illuminate the imaging target. While these systems produce high-quality 3D PA images, they require complex, multi-channel data acquisition (DAQ) systems to acquire data from all transducers. These DAQ systems are often bulky and expensive, significantly limiting the clinical translation of PACT systems for patient care. In this study, we evaluated the feasibility of using a compact and cost-effective Texas Instruments analog front-end DAQ module for multi-single-element PACT systems. By imaging a simple 3D phantom, we demonstrated the capability of this affordable DAQ board, with reconstructed images showing promise for practical and economical solutions in PACT systems. This advancement paves the way for broader applications of PACT in both research and clinical settings. Full article